Performance Evaluation of Neural Network Architectures: The Case of Predicting Foreign Exchange Correlations

Journal of Forecasting, Vol. 24, pp. 403-420, 2006

Posted: 13 Aug 2006

See all articles by An-Sing Chen

An-Sing Chen

National Chung Cheng University - Department of Finance

Mark T. Leung

University of Texas at San Antonio - Department of Management Science and Statistics

Abstract

In the last decade, neural networks have emerged from an esoteric instrument in academic research to a rather common tool assisting auditors, investors, portfolio managers and investment advisors in making critical financial decisions. A better understanding of the network's performance and limitations would help both researchers and practitioners in analysing real-world problems. This study evaluates and compares the performance of models based on two competing neural network architectures, the multi-layered feedforward neural network (MLFN) and general regression neural network (GRNN). Our empirical evaluation measures the network models' strength on the prediction of currency exchange correlation with respect to a variety of statistical tests including RMSE, MAE, U statistic, Theil's decomposition test, Henriksson-Merton market timing test and Fair-Shiller informational content test. Results of experiments suggest that the selection of proper architectural design may contribute directly to the success in neural network forecasting. In addition, market timing tests indicate that both MLFN and GRNN models have economically significant values in predicting the exchange rate correlation. On the other hand, informational content tests discover that the neural network models based on different architectures capture useful information not found in each other and the information sets captured by the two network designs are independent of one another. An auxiliary experiment is developed and confirms the possible synergetic effect from combining forecasts made by the two different network architectures and from incorporating information from an implied correlation model into the neural network forecasts.

Keywords: Time series forecasting, foreign exchange, implied correlation, neural networks, econometric testing

Suggested Citation

Chen, An-Sing and Leung, Mark T., Performance Evaluation of Neural Network Architectures: The Case of Predicting Foreign Exchange Correlations. Available at SSRN: https://ssrn.com/abstract=923906

An-Sing Chen (Contact Author)

National Chung Cheng University - Department of Finance ( email )

Chia-Yi, Taiwan 621
China
+011 886 5 272 0411 (Phone)
+011 886 5 272 0818 (Fax)

Mark T. Leung

University of Texas at San Antonio - Department of Management Science and Statistics ( email )

San Antonio, TX
United States

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